La escritura le ha dado respuestas a la autora María Fernanda Heredia

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(Logincome)

Continuous variable representing natural logarithmic value of per capita household income

Place of Residence (Urban)

Dummy variable capturing individuals who live in the urban areas

Gender

(Gend_male) Dummy variable capturing gender; male=1 and female=0 Age

(Age) Years Continuous variable representing individuals aged 15-64 Square of Age

(Agesquared) Years-squared

Continuous variable capturing the effect of ageing in the sample

(Education) Prim_Educ

Dummy variable indicating persons with primary education

(Education)

Sec_Educ Dummy variable for persons with secondary education (Education)

Tert_Educ

Dummy variable representing persons with tertiary education

(Education) No_Educ

Dummy variable for persons with no form of formal education

Young Children

(kids0004) Individuals from households with children aged 0-4 years Young Children

(kids0509) Individuals from households with children aged 5-9 years Source: Author’s computation

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measures to restore their health status. Individuals incur health costs for treatment of an illness and also take time off work to recuperate An individual who falls ill consequently incurs healthcare costs otherwise referred to as direct costs of illness (Alaba and Alaba, 2009; Mclinden, 2013; Zhang et al 2013). This study examines direct costs components due to malaria as the sum of four basic components; transport costs to the hospital, physician consultation charges, cost of drug and hospitalisation costs (Mclinden, 2013; Zhang et al 2013).

4.4.1 Empirical Estimation of Indirect Costs for a Bout of Malaria (Objective 2) Following the argument that there is productivity loss associated with illness, this study computes indirect cost associated with an episode of malaria using specifications of the HCA presented as

(4.13) Where: represents mean indirect costs, NMD , represents the number of missed work days²⁸, and DE is daily earnings for an individual (Koopmanschap and Rutten, 1996; Joel and Segel, 2006; Alaba and Alaba, 2009; Ghatak et al 2011; Salihu and Sanni, 2013; Mclinden, 2013). Equation 4.15 is estimated only for persons with malaria who are gainfully employed. Earnings of such persons and missed work days are used to compute average figures for productivity loss due to a bout of malaria.

Thus individuals with malaria who did not miss any workday were not included in the sample used to determine productivity loss from malaria.

4.4.2 Empirical Estimation of Direct Cost for a Bout of Malaria (objective 3) For the purpose of computation, direct cost associated with a bout of malaria is determined using the BUA²⁹ given as:

∑ (4.14) Where: represents average direct cost associated with treatment of a bout of malaria for an individual , TEM, represents total expenditure for the treatment of malaria. Total expenditure for treating malaria is determined as a summation of four

2828 This study examines mean workdays lost for a one month period.

29 The BUA has been explained in chapter 3.

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cost types incurred by individuals during treatment. Based on available data, components of direct costs considered are transport costs to the hospital, physician consultation charges, cost of drug and hospitalisation expenditure.

To further determine cost burden for treatment of an episode of malaria, the study measures the proportion of monthly income expended on a bout of malaria as:

(

) (4.15) Where: characterise average percentage value of monthly income for an individual spent on malaria treatment. represents average value of total costs incurred during treatment of one bout of malaria and symbolises the average monthly income for an individual in the sample.

4.4.3 Empirical Estimation of Total cost (for an episode of malaria) as a fraction of GDP (objective 4)

This study estimates the fraction of GDP lost to an episode of malaria using total cost associated with a bout of the illness as a percentage of GDP³⁰. For simplicity, the study computes total direct and indirect costs in relation to the illness assuming every individual of all age groups in Nigeria experiences one bout of malaria per year. This follows from postulations that about half of the adult population in Nigeria experience at least one episode of malaria per year with children under-five years of age having as much as 2 to 4 bouts (FMOH, 2005; UNICEF, 2010).

To achieve this objective, the study computes total costs in relation to Malaria; first, by multiplying mean direct costs with total population size for the entire Nigerian populace³¹. This is to obtain total direct costs of all persons in the population. Total direct cost for all persons in the population is determined as:

(4.16)

30Figures for 2009 nominal GDP were used in conformity with the period for which the HNLSS was conducted

31 National population figures used in the analysis were also obtained using estimates for 2009.

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Where; represents total direct cost for all persons in the population, symbolises direct cost per individual as obtained in equation 4.13 and Pop represents population size.

Second, the study computes indirect costs only for the fraction of individuals in the economically active population who missed workdays due to Malaria. Fraction of persons in the working population who missed workdays is determined by the fraction of individuals in the study sample who missed workdays due to malaria. The study hence computes total indirect costs associated with malaria in three stages; stage 1 indicates determination of number of persons in the economically active population, stage 2 shows computation of proportion of persons in the economically active population who missed work days due to Malaria and stage 3 shows computation of indirect costs for persons who missed workdays while ill with malaria.

First stage:

(4.17)

Where: EA characterised the fraction of economically active population in the total population and X represents the percentage of the economically active population in the total sample surveyed in the HNLSS.

Second stage:

(4.18) Where: EAM symbolises persons in the economically active population who missed workdays due to malaria, M denotes percentage of the economically active respondents who missed workdays from malaria as obtained in the study sample.

Third Stage

(4.19) Where: TIDC implies total indirect cost for an episode of malaria in Nigeria, is defined already as indirect cost for each individual and EAM is as earlier defined.

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Total cost associated with an episode of malaria in Nigeria (TC) is hence estimated as the sum of total direct costs and total indirect costs;

(4.20)

Total cost associated with malaria as a fraction of GDP is hence estimated as

( ) (4.21)